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This conceptual study investigates the adoption of education technology (EdTech) products among college students, focusing on identifying the key factors influencing the adoption process within educational institutions. Technology integration in education has rapidly gained prominence, with EdTech offering innovative solutions to enhance teaching and learning experiences. However, understanding the determinants that affect EdTech adoption remains critical for its successful implementation and impact. This paper aims (1) to identify the factors influencing the adoption of EdTech by college students (2) to create a conceptual model that shows the connections between the elements that lead to college students adopting EdTech.

The research employed a mixed-methods approach, combining qualitative data analysis and conceptual modeling to achieve the objectives. The underlying knowledge required to create a qualitative data gathering tool was obtained through a thorough literature analysis on innovation dissemination, educational psychology and technology adoption. College students, teachers and administrators participated in semi-structured interviews, focus groups and surveys to provide detailed perspectives on their attitudes about and experiences with EdTech. The Scopus and Web of Science databases are searched for relevant information in an organized manner in order to determine the factors influencing the adoption of EdTech. Second, an extended version of the technology adoption model is adopted to develop a qualitative data-based conceptual framework to analyze EdTech adoption in the Indian context.

Overall, by highlighting the critical components that emotionally influence college students' adoption of EdTech products in educational institutions, this course adds to the body of information already in existence. The conceptual framework model serves as a roadmap for educational stakeholders seeking to leverage EdTech effectively to enrich the learning environment and improve educational outcomes. By recognizing the significance of the identified factors, academic institutions can make informed decisions to foster a climate conducive to successful EdTech integration.

A comprehensive conceptual framework model was developed based on qualitative data analysis to illustrate the interrelationships between the identified factors influencing EdTech adoption. This model presents a valuable tool for educational institutions, policymakers and EdTech developers to comprehend the complex dynamics of implementing these technological solutions.

The findings of this study demonstrated a number of important variables that affect the uptake of EdTech products in educational settings. These factors encompassed technological infrastructure, ease of use, perceived usefulness, compatibility with existing academic practices, institutional support, financial constraints and individual attitudes towards technology. Additionally, the research explored the significance of institutional preparation for embracing technological advancements as well as the influence of socio-cultural elements.

The aim of this study was to measure the level of challenges faced by novice teachers in South Africa. Findings suggest a need for professional development courses to upskill teachers with effective pedagogies that can incorporate the social and emotional components into teaching and learning.

This study applied a descriptive research methodology by administering a questionnaire to 143 novice teachers. The data analysis technique was the Rasch model.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. The three major challenges identified are uninvolved parents, discipline problems and a lack of guidance and counselling. These challenges can be related to social and emotional learning (SEL) components.

The study used quantitative methods and discovered the challenges that novice teachers face. If the research uses a combination of qualitative methods, it will be possible to better identify the specific causes of the above three challenges of novice teachers.

Due to the complex nature of South African society, many novice teachers are overwhelmed by the challenges they face when entering the profession. These challenges are often embedded in societal risk factors, which complicate the transition from student teacher to novice teacher. The major challenges identified in this study can be related to SEL components, as the challenges are closely linked to the psychological and social backgrounds of the students. Teachers in this study indicated that they found it difficult to deal with these challenges at the beginning of their careers.

By identifying the challenges facing new teachers in South Africa, they will be better prepared for their work in schools. Therefore, they will improve the above situation to continue developing professionally.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. Teachers in this study indicated that they found it difficult to deal with these challenges in the beginning of their careers. Professional development courses are suggested to help teachers deal with issues such as discipline, uninvolved parents and guidance and counselling effectively. Higher education programmes should also include these topics in their curricula for student teachers. A greater emphasis on training teachers in their pastoral roles is suggested.

The search for new techniques to teach English nowadays has been more than ever. These techniques have to be interesting and enjoyable in order to lower the anxiety levels of students when learning English (Bakhsh, 2016). That is why many scholars and teachers look forward to integrating technology into language teaching. Social media platforms (SMPs) are among these techniques since millions of people around the world utilize them for daily interaction. Yet, teaching English for specific purposes (ESPs) relies on learners’ needs and employs an eclectic approach in delivering its course content. For this reason, the current study reviewed articles that tackled the topic of teaching or learning ESP from SMPs so as to uncover their effect and the attitude or motivation of learners.

The researchers used the PRISMA flowchart model in order to identify, screen and include articles in the study.

The results revealed that SMPs are effective in teaching and learning ESP writing, speaking and vocabulary. Yet, the included studies showed that learners’ attitude toward SMPs is positive as they believe that they are motivating and interesting.

Some aspects of social media have turned out to be beneficial in the learning process and they need further investigation from ESP practitioners and scholars.

According to the study, it is crystal clear that the various social networks and platforms are beneficial and helpful for improving ESP productive skills.

There is a lack of clarity about what constitutes Science, Technology, Engineering, Arts and Mathematics (STEAM) education and what the arts contribute. In this paper the authors discuss a distinct model, theorised from a five-year study of a particular, innovative STEAM education project (The Imagineerium), and developed by the researchers through working with primary school teachers in England within a second project (Teach-Make). The paper examines how teachers implemented this model, the Trowsdale art-making model for education (the TAME), and reflected on its value and positive impact on their planning and pedagogy.

The paper draws on two studies: firstly, a five-year, mixed methods, participative study of The Imagineerium and secondly a participative and collaborative qualitative study of Teach-Make.

Study of The Imagineerium showed strong positive educational outcomes for pupils and an appetite from teachers to translate the approach to the classroom. The Teach-Make project showed that with a clear curriculum model (the TAME) and professional development to improve teachers' planning and active pedagogical skills, they could design and deliver “imagineerium-like” schemes of work in their classrooms. Teachers reported a positive impact on both their own approach to supporting learning, as well as pupil progression and enjoyment.

The paper argues that the TAME, a consolidation of research evidence from The Imagineerium and developed through Teach-Make, offers both a distinctive and effective model for STEAM and broader education, one that is accessible to, valued by and manageable for teachers.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

This study aims to investigate how online teaching of faculty members is affected by technological readiness (TR) of using online teaching platforms. The study sheds light on how many faculty members were ready to use different online platforms during COVID-19 period.

This study used TR measures to determine the impact of optimism regarding the perceived usefulness and ease of usage, impact of innovativeness in terms of perceived usability and ease of use, the influence of discomfort on perceived usefulness and ease of usage, the effect of uncertainty on perceived usefulness and ease of use and the influence of perceived usefulness and ease of use on behavior. An online questionnaire survey was conducted among 255 faculty members of different private universities of Bangladesh. The sample was chosen based on a convenience method. The responses were analyzed using partial least square (PLS) approach with the help of software Smart PLS 3.

The finding supported all of the hypotheses except that discomfort and insecurity have a positive relationship with ease of use and usefulness.

The study will help faculty members in developing their competency in using technologies in their pedagogy. Also, this study will provide some guidelines to the university management in developing adequate technological infrastructure to aid teaching.

The aim of the study was to investigate the faculty members' readiness level with respect to online teaching. The technology assessment model (TAM) was used to determine the readiness index. The study intended to validate the hypotheses regarding the extent to which the faculty members perceived that TAM factors affect Ease of Use and Usefulness of online teaching. Also, this research analyzed the perception of faculty members that Ease of Using online teaching affects its Usefulness. Lastly, the study examined how their perception of Ease of Use and Usefulness affect Intention to Use online as a mode of teaching. It was found from the study that each of the TAM factors, Optimism, Innovativeness, Insecurity and Discomfort has positive and significant contribution on the Ease of Use. On the other hand, Optimism, Innovativeness, Insecurity and Discomfort have positive and significant contributions on the Usefulness. The study also revealed that Ease of Use has positive and significant contribution on the Usefulness. Lastly, it was found that Ease of Use and Usefulness have positive and significant contribution on the Intention to use. Teaching remotely is still a novel concept, and it is more difficult for people who have not done it before. Many teachers became burned out as a result of trying to adjust to new teaching methods, especially after the lockdown began. They were having a difficult time since there was so much ambiguity. When a teacher is well-versed in communication tools, it can improve learning efficiency. When they are properly trained, deploying engaging features of virtual learning, such as audio-visual lessons, quizzes, and so on, becomes simple, and students become eager to learn more. Teachers can plan their classes, prepare and master technology and create innovative and stimulating discussion topics (Mishra et al., 2020). They need to utilize a variety of technological options. They can rehearse virtual classroom management with colleagues if they face any difficulty. All of the aforementioned abilities can be honed with the assistance of an integrated academic system. Teachers can be trained by educational institutions to ensure a smooth learning process through the use of ICT (information and communication technologies) (Scherer et al., 2021; Mishra et al., 2020). The training will assist teachers in efficiently taking online classes. Institutions should ensure that teachers are well-suited to teach online and are skilled at keeping students engaged during remote learning. To make every chapter engaging, aspects such as videos, slides, images and digital copies of books and workbooks can be used. This allows students to receive personalized support and counseling in order to maintain their motivation (Sahu et al., 2022; Lapitan et al., 2021). Every other day, group doubt resolution classes ensure that there are no gaps in learning (Lapitan et al., 2021). All teachers require is a digital mindset, the appropriate tools and a committed approach (Sahu et al., 2022). If teachers can hold their students' attention, they can easily deliver an effective learning experience (Lapitan et al., 2021).

This study was conducted to identify technological preparedness of faculty members of private universities in Bangladesh during COVID-19 period. Some studies were there to assess such kind of preparedness but none of those used TAM and technology readiness model either in isolation or in combination. Also, this paper focused on teachers' readiness in contrast to students' readiness specific to private universities.

Massive open online courses (MOOCs), a Taylorist attempt to automate instruction, help make course delivery more efficient, economical and better. As an implementation of Digital Taylorism Implementation (DTI), MOOCs enable individuals to obtain an occupation-oriented education, equipping them with knowledge and skills needed to stay employable. However, learning through online platforms can induce tremendous amounts of technology-related stress in learners such as complexity of platforms and fears of redundancy. Thus, the aim of this paper is to study how student perceptions of DTI and technostress (TS) influence their perceived employability (PE). The role of TS as a mediator between DTI and PE has also been studied.

Stratified sampling technique has been used to obtain data from 305 students from 6 universities. The effect of DTI and TS on PE, and the role of TS as a mediator, has been examined using the partial least squares (PLS) structural equation modelling approach with SMART PLS 4.0. software. Predictive relevance of the model has been studied using PLSPredict.

Results indicate that TS completely mediates the relationship between DTI and PE. The model has medium predictive relevance.

Learning outcomes from Digitally Taylored programs can be improved with certain reforms that bring the human touch to online learning.

This study extends Taylorism literature by linking DTI to PE of students via technostress as a mediator.

The impact of COVID-19 on tourism destinations has been severe, but a future crisis is never far away. How communities can better prepare for disasters to come in the near future continues to be researched. This research aims to understand the tourism community’s responses to the COVID-19 pandemic and present the Tourism Community Resilience Model as a useful instrument to help communities better respond to disasters in the future.

This research uses a qualitative research approach which seeks to understand phenomena, events, social activities, attitudes, beliefs, perceptions and individual and group opinions that are dynamic in character in accordance with the situation in the field. Research primary data is in the form of Kuta Traditional Village local community responses in enduring the COVID-19 pandemic conducted between January and May 2022. These data were obtained through in-depth observations and interviews involving informants based on purposive sampling, including traditional community leaders, village officials, tourism actors (i.e. street vendors, tourist local guides, taxi drivers and art workers) and tourism community members. We selected the informants who are not only directly impacted by the pandemic, but also some of them have to survive during the pandemic because they do not have other job options. The results of previous research and government data concerning the pandemic and community resilience were needed as secondary data, which were obtained through a study of the literature. The data which had been obtained were further analysed based on the Interpretative Phenomenological Analysis (IPA) technique, which seeks to make meaning of something from the participants’ perspective and the researchers’ perspective as a result there occurs a cognition of a central position.

Based on findings from Bali, Indonesia, this resilience model for the tourism community was created in response to the difficulties and fortitude shown by the community during the COVID-19 pandemic. It comprises four key elements, namely the Local Wisdom Foundation, Resource Management, Government Contributions and External Community Support. These elements are all rooted in the concepts of niskala (spirituality) and sekala (real response); it is these elements that give the tourism community in the Kuta Traditional Village a unique approach, which can inspire other tourism destinations in other countries around the world.

A tourism community resilience model based on local community responses has implications for the process of enriching academic research and community management practices in facing future crisis, particularly by involving local wisdom foundation.

A tourism community resilience model based on local community responses has implications for the process of enriching academic research and community management practices in facing future crisis, particularly by involving local wisdom foundation.

The existence of the resilience model strengthens local community social cohesion, which has been made stronger by the bonds of culture and shared faith in facing disaster. This social cohesion then stimulates the strength of sustainable and long-term community collaboration in the post-pandemic period. For tourism businesses, having strong connections with the local communities is an important condition to thrive.

The value of this research is the Tourism Resilience Community Model, which is a helpful tool to optimise and improve future strategies for dealing with disasters. Illustrated by this Balinese example, this paper emphasises the importance of adding social factors such as niskala and sekala to existing community resilience models. Addressing these local characteristics is the innovative aspect of this paper and will help inspire communities around the world to prepare for future disasters better and build more sustainable and resilient tourism destinations elsewhere.

This study proposes a hierarchic segmentation that develops a tree-based classification model and classifies the cases into groups. This allows for the definition of e-commerce user profiles for each of the groups. Additionally, it facilitates the development of actions to improve the adoption of the online channel that is in such high demand in the current pandemic COVID-19 context.

Regarding the created segments, two extreme segments stand out due to their marked differences and high volume. Segment 3 with 23% of the sample is the group with the most predisposition to use the online channel and is characterised by a high level of trust, more habitual use in comparison with other groups and the belief that its use implies high performance, which indicates they believe it to be useful, quick and helpful for more an effective shopping experience. The other extreme is found in segment 7. This group makes up 17.7% of the total and is the most reluctant to use the online channel. These users are characterised by the complete opposite: they have a low level of trust in this channel. However, the effort expectancy is low, i.e. they consider that the adoption of the online channel does not involve many difficulties in its learning and use. Nevertheless, they use it less regularly than the others.

Based on the conclusions reached in this study, in the current pandemic context in which consumer demand for online shopping channels for all types of products is on the rise, it is recommended that companies focus on the following aspects. It is essential to build trust with the user and show them the real benefits of e-commerce, how it would improve their life and why they should use it. Additionally, it is vital that the user perceives it as an easy procedure that does not require a significant learning curve. Other fundamental aspects would be to reduce any uncertainty the user might have about the online shopping process, to make it as easy as possible, and to design a simple, intuitive and user-friendly interface. It is also recommendable to manage data usage efficiently. To do so, the authors recommend asking the user for the least amount of information possible, offering a data protection policy and assuring them that their information will not be misused nor shared with third parties. All of this provides a series of facilities to modify the online shopping habits of users.

As in most of the research, this study presents a series of limitations that should be debated and that could open future lines of investigation. Firstly, regarding the sample used that was limited to two neighbouring countries with similar profiles a priori; it would be necessary to compare their possible cultural differences according to Hofstede's dimensions as well as increase the number of European countries being analysed to reach a more generalised conclusions. Secondly, the variables used are a combination of those derived from the UTAUT2 model and others suggested in the literature as decisive in technology adoption by users, in this sense other theories and variables could be incorporated to complete a more holistic model.

This work contributes in a general way to (1) analysing the intention to use e-commerce platforms from a set of antecedents previously defined by their importance, after a period of economic and social restrictions derived from the pandemic; (2) determination of customer segments from the classification made by the CHAID analysis; (3) characterisation of the previously defined segments through the successive divisions that were proposed in the analysis carried out.

Other fundamental aspects would be to reduce any uncertainty the user might have about the online shopping process to make it as easy as possible, and to design a simple, intuitive, and user-friendly interface. It is also recommended to manage data usage efficiently. To do so, the authors recommend asking the user for the least amount of information possible, offering a data protection policy, and assuring them that their information will not be misused or shared with third parties.

The results obtained have allowed us to establish predictive and explanatory models of the behaviour of the segments and profiles created, which will help companies to improve their relationships with online customers in the coming years.

本研究擬提出一個會發展基於樹的分類模型、以及會把案例歸入不同的類別的層次細分。這讓我們能為每個類別考慮到電子商務用戶輪廓的定義和解釋；這亦促進我們優化採用在線渠道的發展工作,而在線渠道於現時2019冠狀病毒病肆虐的情況下,實在供不應求。

就創設的細分而言,兩個極端的細分因其明顯的差別和大批量而顯得突出。佔樣本百分之二十三的細分3是擁有最大使用在線渠道傾向的細分,而細分3的特徵包括他們對在線渠道呈高信任度,比其他類別更習慣地使用,以及其相信使用在線渠道會帶來更高的績效,這表示他們相信使用在線渠道是有效的,是快捷的,是可幫助帶來成功的購物體驗的。另外的極端在細分7內發現。這類別佔整體的百分之十七點七,而他們是最不願意使用在線渠道的類別。這類別的特徵和前述的剛剛相反：他們對在線渠道的信任程度是低的,唯其努力期望是低的,也就是說,他們認為使用在線渠道是不會涉及很多在學習上或在實際應用上的困難。即使是這樣,他們較其他人卻較少使用在線渠道。

基於研究的結論,我們的建議是：於目前大流行肆虐期間,消費者對於以在線渠道網購各類商品的需求不斷增加,企業應聚焦以下的範疇：企業必須建立消費者對電子商務的信心,並為他們展示電子商務的真正好處；企業也必須使消費者明瞭電子商務如何能改善其生活,以及他們為何要使用電子商務。更重要的是使消費者覺得使用電子商務是輕而易舉的,又不涉及陡峭的學習曲線。凡此種種,就成為消費者改變其網上購物習慣的動力和誘因。至於其他基本的考慮,包括減輕消費者對使用電子商務的不確定情緒,使電子商務易於使用,以及設計一個簡易的、憑直覺能知曉的、方便使用的介面。另外,值得推薦的是、數據使用情況須有效地管理。為此,我們建議應儘量向使用者索取最低限度的資料,為他們提供資料保護政策,保證他們的資料不會被濫用或與第三者分享。

與其他大多數的研究一樣,本研究展現了一系列值得辯論的局限,而這些局限或許會開展未來研究的領域。首先,考慮到使用了一個局限於兩個以因及果演繹而成的、概況相似的相鄰國家為樣本,我們或許需要根據霍夫斯泰德文化維度理論對這兩個國家進行比較,以瞭解它們的文化差異；另外,為求能達致可普遍適用的結論,我們也需把被分析的歐洲國家的數目增加。其次,被使用的變數是兩組變數的組合,他們是從UTAUT2模型中取得的變數,以及在有關的文獻裡,就技術採用而言、使用者認為是重要的變數。就此而言,若其他的理論和變數能被包含其中,則達致的模型將會是一個更為整體的模型。

本研究就一般而言有以下的貢獻：(一) 、 在因大流行病而引起的經濟和社會限制實施時期後,研究人員分析人們如何從一套過去被認定是電子商務平台的重要前身而選擇使用電子商務平台,本研究對這方面的分析作出了貢獻；(二) 、本研究幫助確定從透過CHAID分析而來的分類中得到的顧客細分；(三) 、本研究透過進行連續分解、幫助歸納過去被認定的細分的特徵。

企業必須建立消費者對電子商務的信心,並為他們展示電子商務的真正好處；企業也必須使消費者明瞭電子商務如何能改善其生活,以及他們為何要使用電子商務。更重要的是使消費者覺得使用電子商務是輕而易舉的,又不涉及陡峭的學習曲線。凡此種種,就成為消費者改變其網上購物習慣的動力和誘因。至於其他基本的考慮,包括減輕消費者對使用電子商務的不確定情緒,使電子商務易於使用,以及設計一個簡易的、憑直覺能知曉的、方便使用的介面。另外,值得推薦的是、數據使用情況須有效地管理。為此,我們建議應儘量向使用者索取最低限度的資料,為他們提供資料保護政策,保證他們的資料不會被濫用或與第三者分享。

本研究所得的結果,讓我們可以建立多個模型、以預測並解說有關的市場部分的行為和被創建的消費者簡介,這會幫助企業改善它們今後與網上顧客的關係。